De-noising digital radiological images

a digital radiological image and de-noising technology, applied in the field of de-noising digital radiological images, can solve the problems of insufficient computational cost, de-noising of traditional wavelet-based de-noising techniques, and degraded visual performance of most of these techniques, and achieve the effect of effective de-noising

Inactive Publication Date: 2005-11-24
1370509 ALBERTA
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  • Abstract
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AI Technical Summary

Benefits of technology

[0004] It is an object of the invention to provide a digital image de-noising method that improves upon currently available methods. One particular object of the invention is to provide an effective de-noising method for digital radiological images that is computationally tractable.

Problems solved by technology

Despite the high PSNR values, most of these techniques have their visual performance degraded by the introduction of noticeable artifacts which may limit their use in de-noising of medical images.
The common cause of artifacts in the traditional wavelet-based de-noising techniques is due to the pseudo-Gibbs phenomenon which is caused by the lack of translation invariance of the wavelet method.
Although this problem can be avoided by using an undecimated DWT, it is too computationally expensive.

Method used

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Embodiment Construction

[0024] According to one embodiment of the invention and referring to FIG. 1, a method for de-noising radiographic images starts by pre-processing an original image using Anscombe's variance stabilizing transformation, which acts as if the data arose from a Gaussian white noise model. The image is then decomposed in different sub-bands of frequency and orientation responses using an overcomplete dual-tree complex wavelet transform (DT-CWT). By using the DT-CWT, visual artifacts usually present in an image transformed by the traditional DWT are significantly minimized, with the advantage of having a task that is still tractable in terms of computation time. A Hidden Markov Tree (HMT) model is used to describe the correlation among the wavelet coefficients by modeling their marginal distribution and thus improving the discrimination between noisy and singularity pixels in an image. Finally, the modified wavelet coefficients are transformed back into the original domain in order to get ...

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Abstract

This invention relates to a method for de-noising digital radiographic images based upon a wavelet-domain Hidden Markov Tree (HMT) model. The method uses the Anscombe's transformation to adjust the original image to a Gaussian noise model. The image is then decomposed in different sub-bands of frequency and orientation responses using a dual-tree complex wavelet transform, and the HMT is used to model the marginal distribution of the wavelet coefficients. Two different methods were used to denoise the wavelet coefficients. Finally, the modified wavelet coefficients are transformed back into the original domain to get the de-noised image.

Description

FIELD OF THE INVENTION [0001] This invention relates generally to de-noising digital radiological images. BACKGROUND OF THE INVENTION [0002] General image de-noising techniques based upon the traditional (orthogonal, maximally-decimated) discrete wavelet-transform (DWT) have proved to provide the state-of-the-art in de-noising performance, in terms of peak signal-to-noise ratio (PSNR), according to many papers presented in the literature, e.g. Crouse M, Nowak R, Baraniuk R (1998) Wavelet-based statistical signal processing using hidden Markov models. IEEE Transactions on Signal Processing 46:886-902, Donoho D (1995) De-noising by soft-thresholding; IEEE Transactions on Information Theory 41:613-627, and Romberg J, Choi H, Baraniuk R (2001) Bayesian tree-structured image modeling using wavelet-domain hidden Markov models; and, IEEE Transactions on Image Processing 10:1056-1068. The basic idea behind this image-de-noising approach is to decompose the noisy image by using a wavelet tra...

Claims

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Application Information

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Patent Type & Authority Applications(United States)
IPC IPC(8): A61B6/00G06T5/00G06V10/30
CPCG06K9/40G06T2207/30008G06T5/002A61B6/583G06T2207/10116G06T2207/20064G06T2207/20081G06T5/10G06V10/30
Inventor FERRARI, RICARDO J.WINSOR, ROBIN
Owner 1370509 ALBERTA
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